Overview

Dataset statistics

Number of variables29
Number of observations9240
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 MiB
Average record size in memory232.0 B

Variable types

Numeric12
Categorical17

Alerts

14 has constant value "0.0" Constant
20 has constant value "0.0" Constant
22 has constant value "0.0" Constant
23 has constant value "0.0" Constant
26 has constant value "0.0" Constant
7 is highly correlated with 28High correlation
15 is highly correlated with 16High correlation
16 is highly correlated with 15High correlation
28 is highly correlated with 7High correlation
7 is highly correlated with 28High correlation
15 is highly correlated with 16High correlation
16 is highly correlated with 15High correlation
28 is highly correlated with 7High correlation
7 is highly correlated with 28High correlation
15 is highly correlated with 16High correlation
16 is highly correlated with 15High correlation
28 is highly correlated with 7High correlation
16 is highly correlated with 23 and 4 other fieldsHigh correlation
23 is highly correlated with 16 and 15 other fieldsHigh correlation
14 is highly correlated with 16 and 15 other fieldsHigh correlation
26 is highly correlated with 16 and 15 other fieldsHigh correlation
20 is highly correlated with 16 and 15 other fieldsHigh correlation
27 is highly correlated with 23 and 5 other fieldsHigh correlation
5 is highly correlated with 23 and 4 other fieldsHigh correlation
15 is highly correlated with 23 and 4 other fieldsHigh correlation
6 is highly correlated with 23 and 4 other fieldsHigh correlation
12 is highly correlated with 23 and 4 other fieldsHigh correlation
19 is highly correlated with 23 and 4 other fieldsHigh correlation
18 is highly correlated with 23 and 4 other fieldsHigh correlation
17 is highly correlated with 23 and 4 other fieldsHigh correlation
2 is highly correlated with 23 and 5 other fieldsHigh correlation
13 is highly correlated with 23 and 4 other fieldsHigh correlation
22 is highly correlated with 16 and 15 other fieldsHigh correlation
4 is highly correlated with 23 and 4 other fieldsHigh correlation
2 is highly correlated with 3 and 1 other fieldsHigh correlation
3 is highly correlated with 2 and 2 other fieldsHigh correlation
4 is highly correlated with 7 and 1 other fieldsHigh correlation
5 is highly correlated with 16 and 1 other fieldsHigh correlation
6 is highly correlated with 21 and 1 other fieldsHigh correlation
7 is highly correlated with 4 and 1 other fieldsHigh correlation
9 is highly correlated with 2 and 1 other fieldsHigh correlation
10 is highly correlated with 3High correlation
11 is highly correlated with 24High correlation
15 is highly correlated with 16 and 1 other fieldsHigh correlation
16 is highly correlated with 5 and 2 other fieldsHigh correlation
17 is highly correlated with 5 and 2 other fieldsHigh correlation
21 is highly correlated with 6High correlation
24 is highly correlated with 6 and 1 other fieldsHigh correlation
27 is highly correlated with 3 and 1 other fieldsHigh correlation
28 is highly correlated with 4 and 1 other fieldsHigh correlation
0 is uniformly distributed Uniform
1 is uniformly distributed Uniform
0 has unique values Unique
1 has unique values Unique

Reproduction

Analysis started2021-10-28 13:46:53.846680
Analysis finished2021-10-28 13:47:51.606127
Duration57.76 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

0
Real number (ℝ)

UNIFORM
UNIQUE

Distinct9240
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.689856448 × 10-19
Minimum-1.731863366
Maximum1.731863366
Zeros0
Zeros (%)0.0%
Negative4620
Negative (%)50.0%
Memory size72.3 KiB
2021-10-28T19:17:51.859283image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1.731863366
5-th percentile-1.55867703
Q1-0.8659316832
median0
Q30.8659316832
95-th percentile1.55867703
Maximum1.731863366
Range3.463726733
Interquartile range (IQR)1.731863366

Descriptive statistics

Standard deviation1.000054117
Coefficient of variation (CV)1.300484767 × 1018
Kurtosis-1.2
Mean7.689856448 × 10-19
Median Absolute Deviation (MAD)0.8660254089
Skewness2.307331518 × 10-18
Sum4.263256415 × 10-14
Variance1.000108237
MonotonicityNot monotonic
2021-10-28T19:17:52.187402image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.56216671021
 
< 0.1%
0.64314570951
 
< 0.1%
1.6058960341
 
< 0.1%
-0.97680917871
 
< 0.1%
0.65476769551
 
< 0.1%
-1.4776792851
 
< 0.1%
-0.24612367141
 
< 0.1%
-1.5991477841
 
< 0.1%
-0.035053409411
 
< 0.1%
-1.2399909261
 
< 0.1%
Other values (9230)9230
99.9%
ValueCountFrequency (%)
-1.7318633661
< 0.1%
-1.7314884641
< 0.1%
-1.7311135611
< 0.1%
-1.7307386581
< 0.1%
-1.7303637551
< 0.1%
-1.7299888521
< 0.1%
-1.729613951
< 0.1%
-1.7292390471
< 0.1%
-1.7288641441
< 0.1%
-1.7284892411
< 0.1%
ValueCountFrequency (%)
1.7318633661
< 0.1%
1.7314884641
< 0.1%
1.7311135611
< 0.1%
1.7307386581
< 0.1%
1.7303637551
< 0.1%
1.7299888521
< 0.1%
1.729613951
< 0.1%
1.7292390471
< 0.1%
1.7288641441
< 0.1%
1.7284892411
< 0.1%

1
Real number (ℝ)

UNIFORM
UNIQUE

Distinct9240
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.844928224 × 10-19
Minimum-1.731863366
Maximum1.731863366
Zeros0
Zeros (%)0.0%
Negative4620
Negative (%)50.0%
Memory size72.3 KiB
2021-10-28T19:17:52.468630image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1.731863366
5-th percentile-1.55867703
Q1-0.8659316832
median0
Q30.8659316832
95-th percentile1.55867703
Maximum1.731863366
Range3.463726733
Interquartile range (IQR)1.731863366

Descriptive statistics

Standard deviation1.000054117
Coefficient of variation (CV)-2.600969534 × 1018
Kurtosis-1.2
Mean-3.844928224 × 10-19
Median Absolute Deviation (MAD)0.8660254089
Skewness-4.922307238 × 10-17
Sum1.421085472 × 10-14
Variance1.000108237
MonotonicityNot monotonic
2021-10-28T19:17:52.734221image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.33797485111
 
< 0.1%
-0.57003966851
 
< 0.1%
0.83959476331
 
< 0.1%
1.5762787151
 
< 0.1%
0.13365283911
 
< 0.1%
0.95881384551
 
< 0.1%
-0.13590225571
 
< 0.1%
1.4844275351
 
< 0.1%
-0.51305444681
 
< 0.1%
-1.08778041
 
< 0.1%
Other values (9230)9230
99.9%
ValueCountFrequency (%)
-1.7318633661
< 0.1%
-1.7314884641
< 0.1%
-1.7311135611
< 0.1%
-1.7307386581
< 0.1%
-1.7303637551
< 0.1%
-1.7299888521
< 0.1%
-1.729613951
< 0.1%
-1.7292390471
< 0.1%
-1.7288641441
< 0.1%
-1.7284892411
< 0.1%
ValueCountFrequency (%)
1.7318633661
< 0.1%
1.7314884641
< 0.1%
1.7311135611
< 0.1%
1.7307386581
< 0.1%
1.7303637551
< 0.1%
1.7299888521
< 0.1%
1.729613951
< 0.1%
1.7292390471
< 0.1%
1.7288641441
< 0.1%
1.7284892411
< 0.1%

2
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size72.3 KiB
0.4695717705419017
4886 
-1.108763318511271
3580 
2.0479068595950745
718 
3.626241948648247
 
55
5.204577037701419
 
1

Length

Max length18
Median length18
Mean length17.99393939
Min length17

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row-1.108763318511271
2nd row-1.108763318511271
3rd row0.4695717705419017
4th row0.4695717705419017
5th row0.4695717705419017

Common Values

ValueCountFrequency (%)
0.46957177054190174886
52.9%
-1.1087633185112713580
38.7%
2.0479068595950745718
 
7.8%
3.62624194864824755
 
0.6%
5.2045770377014191
 
< 0.1%

Length

2021-10-28T19:17:53.031074image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-28T19:17:53.187311image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.46957177054190174886
52.9%
1.1087633185112713580
38.7%
2.0479068595950745718
 
7.8%
3.62624194864824755
 
0.6%
5.2045770377014191
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

3
Real number (ℝ)

HIGH CORRELATION

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.162129556 × 10-16
Minimum-1.38959267
Maximum5.194938749
Zeros0
Zeros (%)0.0%
Negative5508
Negative (%)59.6%
Memory size72.3 KiB
2021-10-28T19:17:53.452938image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1.38959267
5-th percentile-1.060366099
Q1-1.060366099
median-0.4019129567
Q30.5857667561
95-th percentile1.90267304
Maximum5.194938749
Range6.584531419
Interquartile range (IQR)1.646132855

Descriptive statistics

Standard deviation1.000054117
Coefficient of variation (CV)-8.605358259 × 1015
Kurtosis0.7155208158
Mean-1.162129556 × 10-16
Median Absolute Deviation (MAD)0.6584531419
Skewness0.9640193536
Sum-9.094947018 × 10-13
Variance1.000108237
MonotonicityNot monotonic
2021-10-28T19:17:53.640409image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
-0.40191295672904
31.4%
-1.0603660992543
27.5%
0.58576675611755
19.0%
0.9149933271154
 
12.5%
1.90267304534
 
5.8%
3.219579324142
 
1.5%
2.231899611125
 
1.4%
-0.731139527755
 
0.6%
3.5488058956
 
0.1%
4.2072590375
 
0.1%
Other values (11)17
 
0.2%
ValueCountFrequency (%)
-1.389592674
 
< 0.1%
-1.0603660992543
27.5%
-0.731139527755
 
0.6%
-0.40191295672904
31.4%
-0.072686385792
 
< 0.1%
0.25654018521
 
< 0.1%
0.58576675611755
19.0%
0.9149933271154
 
12.5%
1.2442198981
 
< 0.1%
1.5734464692
 
< 0.1%
ValueCountFrequency (%)
5.1949387491
 
< 0.1%
4.8657121781
 
< 0.1%
4.5364856071
 
< 0.1%
4.2072590375
 
0.1%
3.8780324661
 
< 0.1%
3.5488058956
 
0.1%
3.219579324142
1.5%
2.8903527531
 
< 0.1%
2.5611261822
 
< 0.1%
2.231899611125
1.4%

4
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size72.3 KiB
-0.29375504958364534
8506 
3.4041968007608823
 
734

Length

Max length20
Median length20
Mean length19.84112554
Min length18

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.29375504958364534
2nd row-0.29375504958364534
3rd row-0.29375504958364534
4th row-0.29375504958364534
5th row-0.29375504958364534

Common Values

ValueCountFrequency (%)
-0.293755049583645348506
92.1%
3.4041968007608823734
 
7.9%

Length

2021-10-28T19:17:53.937257image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-28T19:17:54.171621image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.293755049583645348506
92.1%
3.4041968007608823734
 
7.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

5
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size72.3 KiB
-0.014713839651479712
9238 
67.96322535018479
 
2

Length

Max length21
Median length21
Mean length20.9991342
Min length17

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.014713839651479712
2nd row-0.014713839651479712
3rd row-0.014713839651479712
4th row-0.014713839651479712
5th row-0.014713839651479712

Common Values

ValueCountFrequency (%)
-0.0147138396514797129238
> 99.9%
67.963225350184792
 
< 0.1%

Length

2021-10-28T19:17:54.405995image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-28T19:17:54.577858image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0147138396514797129238
> 99.9%
67.963225350184792
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

6
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size72.3 KiB
-0.7918630028733495
5679 
1.2628447046665972
3561 

Length

Max length19
Median length19
Mean length18.61461039
Min length18

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.7918630028733495
2nd row-0.7918630028733495
3rd row1.2628447046665972
4th row-0.7918630028733495
5th row1.2628447046665972

Common Values

ValueCountFrequency (%)
-0.79186300287334955679
61.5%
1.26284470466659723561
38.5%

Length

2021-10-28T19:17:54.734096image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-28T19:17:54.905938image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.79186300287334955679
61.5%
1.26284470466659723561
38.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

7
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.922464112 × 10-17
Minimum-2.101005137
Maximum2.278137672
Zeros0
Zeros (%)0.0%
Negative4690
Negative (%)50.8%
Memory size72.3 KiB
2021-10-28T19:17:55.077804image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2.101005137
5-th percentile-1.553612286
Q1-0.732523009
median-0.185130158
Q31.18335197
95-th percentile1.18335197
Maximum2.278137672
Range4.379142808
Interquartile range (IQR)1.915874979

Descriptive statistics

Standard deviation1.000054117
Coefficient of variation (CV)-5.201939067 × 1016
Kurtosis-1.357257617
Mean-1.922464112 × 10-17
Median Absolute Deviation (MAD)0.547392851
Skewness-0.04841244017
Sum0
Variance1.000108237
MonotonicityNot monotonic
2021-10-28T19:17:55.296534image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
-0.7325230093540
38.3%
1.183351972745
29.7%
0.3622626931973
 
10.5%
0.6359591186640
 
6.9%
-1.827308711428
 
4.6%
-1.553612286326
 
3.5%
-1.27991586267
 
2.9%
-0.185130158116
 
1.3%
1.45704839593
 
1.0%
1.73074482161
 
0.7%
Other values (7)51
 
0.6%
ValueCountFrequency (%)
-2.1010051379
 
0.1%
-1.827308711428
 
4.6%
-1.553612286326
 
3.5%
-1.27991586267
 
2.9%
-1.0062194342
 
< 0.1%
-0.7325230093540
38.3%
-0.45882658352
 
< 0.1%
-0.185130158116
 
1.3%
0.0885662675630
 
0.3%
0.3622626931973
 
10.5%
ValueCountFrequency (%)
2.2781376721
 
< 0.1%
2.0044412466
 
0.1%
1.73074482161
 
0.7%
1.45704839593
 
1.0%
1.183351972745
29.7%
0.90965554411
 
< 0.1%
0.6359591186640
 
6.9%
0.3622626931973
 
10.5%
0.0885662675630
 
0.3%
-0.185130158116
 
1.3%

8
Real number (ℝ)

Distinct38
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.81689054 × 10-16
Minimum-4.053788277
Maximum8.020063379
Zeros0
Zeros (%)0.0%
Negative9005
Negative (%)97.5%
Memory size72.3 KiB
2021-10-28T19:17:55.562145image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-4.053788277
5-th percentile-0.1379444968
Q1-0.1379444968
median-0.1379444968
Q3-0.1379444968
95-th percentile-0.1379444968
Maximum8.020063379
Range12.07385166
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.000054117
Coefficient of variation (CV)3.550205813 × 1015
Kurtosis40.88078781
Mean2.81689054 × 10-16
Median Absolute Deviation (MAD)0
Skewness6.154906815
Sum2.387423592 × 10-12
Variance1.000108237
MonotonicityNot monotonic
2021-10-28T19:17:55.812124image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
-0.13794449688953
96.9%
7.36742274969
 
0.7%
6.71478211953
 
0.6%
4.43053991424
 
0.3%
4.10421959921
 
0.2%
7.04110243415
 
0.2%
-3.72746796213
 
0.1%
3.45157896910
 
0.1%
-3.4011476477
 
0.1%
-0.46426481187
 
0.1%
Other values (28)68
 
0.7%
ValueCountFrequency (%)
-4.0537882772
 
< 0.1%
-3.72746796213
0.1%
-3.4011476477
0.1%
-3.0748273322
 
< 0.1%
-2.7485070172
 
< 0.1%
-2.4221867024
 
< 0.1%
-2.0958663872
 
< 0.1%
-1.7695460721
 
< 0.1%
-1.4432257576
0.1%
-1.1169054424
 
< 0.1%
ValueCountFrequency (%)
8.0200633795
 
0.1%
7.6937430641
 
< 0.1%
7.36742274969
0.7%
7.04110243415
 
0.2%
6.71478211953
0.6%
6.3884618042
 
< 0.1%
6.0621414891
 
< 0.1%
5.7358211741
 
< 0.1%
5.4095008593
 
< 0.1%
5.0831805441
 
< 0.1%

9
Real number (ℝ)

HIGH CORRELATION

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.022750908 × 10-16
Minimum-2.033634205
Maximum1.467753383
Zeros0
Zeros (%)0.0%
Negative4389
Negative (%)47.5%
Memory size72.3 KiB
2021-10-28T19:17:56.312090image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2.033634205
5-th percentile-1.839112672
Q1-0.6719834765
median0.3006241868
Q30.8841887848
95-th percentile1.27323185
Maximum1.467753383
Range3.501387588
Interquartile range (IQR)1.556172261

Descriptive statistics

Standard deviation1.000054117
Coefficient of variation (CV)-9.778080954 × 1015
Kurtosis-1.030871511
Mean-1.022750908 × 10-16
Median Absolute Deviation (MAD)0.583564598
Skewness-0.5026098765
Sum-1.023181539 × 10-12
Variance1.000108237
MonotonicityNot monotonic
2021-10-28T19:17:56.593323image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0.88418878483380
36.6%
-1.255548074976
 
10.6%
-0.6719834765848
 
9.2%
-0.08841887848838
 
9.1%
0.3006241868503
 
5.4%
-1.839112672403
 
4.4%
-0.4774619438366
 
4.0%
1.27323185349
 
3.8%
-2.033634205338
 
3.7%
0.1061026542203
 
2.2%
Other values (9)1036
 
11.2%
ValueCountFrequency (%)
-2.033634205338
 
3.7%
-1.839112672403
4.4%
-1.6445911457
 
0.6%
-1.450069607112
 
1.2%
-1.255548074976
10.6%
-1.061026542159
 
1.7%
-0.8665050091114
 
1.2%
-0.6719834765848
9.2%
-0.4774619438366
 
4.0%
-0.2829404112178
 
1.9%
ValueCountFrequency (%)
1.467753383203
 
2.2%
1.27323185349
 
3.8%
1.07871031840
 
0.4%
0.88418878483380
36.6%
0.689667252273
 
0.8%
0.4951457195100
 
1.1%
0.3006241868503
 
5.4%
0.1061026542203
 
2.2%
-0.08841887848838
 
9.1%
-0.2829404112178
 
1.9%

10
Real number (ℝ)

HIGH CORRELATION

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.441529422 × 10-16
Minimum-4.179125949
Maximum2.354244617
Zeros0
Zeros (%)0.0%
Negative1265
Negative (%)13.7%
Memory size72.3 KiB
2021-10-28T19:17:56.856639image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-4.179125949
5-th percentile-2.00133576
Q10.1764544287
median0.1764544287
Q30.1764544287
95-th percentile1.628314555
Maximum2.354244617
Range6.533370566
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.000054117
Coefficient of variation (CV)-4.096015014 × 1015
Kurtosis3.478297758
Mean-2.441529422 × 10-16
Median Absolute Deviation (MAD)0
Skewness-1.09992126
Sum-2.046363079 × 10-12
Variance1.000108237
MonotonicityNot monotonic
2021-10-28T19:17:57.044125image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.17645442877250
78.5%
-2.00133576808
 
8.7%
2.354244617348
 
3.8%
1.628314555310
 
3.4%
-1.275405697186
 
2.0%
-2.727265823152
 
1.6%
-4.17912594970
 
0.8%
0.902384491667
 
0.7%
-3.45319588626
 
0.3%
-0.549475634223
 
0.2%
ValueCountFrequency (%)
-4.17912594970
 
0.8%
-3.45319588626
 
0.3%
-2.727265823152
 
1.6%
-2.00133576808
 
8.7%
-1.275405697186
 
2.0%
-0.549475634223
 
0.2%
0.17645442877250
78.5%
0.902384491667
 
0.7%
1.628314555310
 
3.4%
2.354244617348
 
3.8%
ValueCountFrequency (%)
2.354244617348
 
3.8%
1.628314555310
 
3.4%
0.902384491667
 
0.7%
0.17645442877250
78.5%
-0.549475634223
 
0.2%
-1.275405697186
 
2.0%
-2.00133576808
 
8.7%
-2.727265823152
 
1.6%
-3.45319588626
 
0.3%
-4.17912594970
 
0.8%

11
Real number (ℝ)

HIGH CORRELATION

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.244163435 × 10-16
Minimum-11.31251645
Maximum2.675981488
Zeros0
Zeros (%)0.0%
Negative8534
Negative (%)92.4%
Memory size72.3 KiB
2021-10-28T19:17:57.247240image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-11.31251645
5-th percentile-0.1217180989
Q1-0.1217180989
median-0.1217180989
Q3-0.1217180989
95-th percentile2.675981488
Maximum2.675981488
Range13.98849794
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.000054117
Coefficient of variation (CV)1.601582225 × 1015
Kurtosis24.30109576
Mean6.244163435 × 10-16
Median Absolute Deviation (MAD)0
Skewness-1.34831789
Sum5.911715562 × 10-12
Variance1.000108237
MonotonicityNot monotonic
2021-10-28T19:17:57.481592image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
-0.12171809898290
89.7%
2.675981488706
 
7.6%
-2.919417686210
 
2.3%
-5.71711727416
 
0.2%
-8.51481686110
 
0.1%
-11.312516458
 
0.1%
ValueCountFrequency (%)
-11.312516458
 
0.1%
-8.51481686110
 
0.1%
-5.71711727416
 
0.2%
-2.919417686210
 
2.3%
-0.12171809898290
89.7%
2.675981488706
 
7.6%
ValueCountFrequency (%)
2.675981488706
 
7.6%
-0.12171809898290
89.7%
-2.919417686210
 
2.3%
-5.71711727416
 
0.2%
-8.51481686110
 
0.1%
-11.312516458
 
0.1%

12
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size72.3 KiB
-0.01699069165076462
9237 
39.231507021615506
 
2
78.48000473488179
 
1

Length

Max length20
Median length20
Mean length19.99924242
Min length17

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row-0.01699069165076462
2nd row-0.01699069165076462
3rd row-0.01699069165076462
4th row-0.01699069165076462
5th row-0.01699069165076462

Common Values

ValueCountFrequency (%)
-0.016990691650764629237
> 99.9%
39.2315070216155062
 
< 0.1%
78.480004734881791
 
< 0.1%

Length

2021-10-28T19:17:57.778451image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-28T19:17:57.934707image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.016990691650764629237
> 99.9%
39.2315070216155062
 
< 0.1%
78.480004734881791
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

13
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size72.3 KiB
-0.03895446935658098
9226 
25.670995305986864
 
14

Length

Max length20
Median length20
Mean length19.9969697
Min length18

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.03895446935658098
2nd row-0.03895446935658098
3rd row-0.03895446935658098
4th row-0.03895446935658098
5th row-0.03895446935658098

Common Values

ValueCountFrequency (%)
-0.038954469356580989226
99.8%
25.67099530598686414
 
0.2%

Length

2021-10-28T19:17:58.137800image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-28T19:17:58.309664image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.038954469356580989226
99.8%
25.67099530598686414
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

14
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size72.3 KiB
0.0
9240 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.09240
100.0%

Length

2021-10-28T19:17:58.512775image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-28T19:17:58.669012image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.09240
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

15
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size72.3 KiB
-0.014713839651479712
9238 
67.96322535018479
 
2

Length

Max length21
Median length21
Mean length20.9991342
Min length17

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.014713839651479712
2nd row-0.014713839651479712
3rd row-0.014713839651479712
4th row-0.014713839651479712
5th row-0.014713839651479712

Common Values

ValueCountFrequency (%)
-0.0147138396514797129238
> 99.9%
67.963225350184792
 
< 0.1%

Length

2021-10-28T19:17:58.887762image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-28T19:17:59.059627image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0147138396514797129238
> 99.9%
67.963225350184792
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

16
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size72.3 KiB
-0.010403692717823858
9239 
96.11971701997463
 
1

Length

Max length21
Median length21
Mean length20.9995671
Min length17

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row-0.010403692717823858
2nd row-0.010403692717823858
3rd row-0.010403692717823858
4th row-0.010403692717823858
5th row-0.010403692717823858

Common Values

ValueCountFrequency (%)
-0.0104036927178238589239
> 99.9%
96.119717019974631
 
< 0.1%

Length

2021-10-28T19:17:59.231488image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-28T19:17:59.434584image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0104036927178238589239
> 99.9%
96.119717019974631
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

17
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size72.3 KiB
-0.010403692717823858
9239 
96.11971701997463
 
1

Length

Max length21
Median length21
Mean length20.9995671
Min length17

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row-0.010403692717823858
2nd row-0.010403692717823858
3rd row-0.010403692717823858
4th row-0.010403692717823858
5th row-0.010403692717823858

Common Values

ValueCountFrequency (%)
-0.0104036927178238589239
> 99.9%
96.119717019974631
 
< 0.1%

Length

2021-10-28T19:17:59.637692image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-28T19:17:59.856429image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0104036927178238589239
> 99.9%
96.119717019974631
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

18
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size72.3 KiB
-0.020810764446485858
9236 
48.052055106935846
 
4

Length

Max length21
Median length21
Mean length20.9987013
Min length18

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.020810764446485858
2nd row-0.020810764446485858
3rd row-0.020810764446485858
4th row-0.020810764446485858
5th row-0.020810764446485858

Common Values

ValueCountFrequency (%)
-0.0208107644464858589236
> 99.9%
48.0520551069358464
 
< 0.1%

Length

2021-10-28T19:18:00.090803image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-28T19:18:00.247020image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0208107644464858589236
> 99.9%
48.0520551069358464
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

19
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size72.3 KiB
-0.02753452584887775
9233 
36.31803959466975
 
7

Length

Max length20
Median length20
Mean length19.99772727
Min length17

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.02753452584887775
2nd row-0.02753452584887775
3rd row-0.02753452584887775
4th row-0.02753452584887775
5th row-0.02753452584887775

Common Values

ValueCountFrequency (%)
-0.027534525848877759233
99.9%
36.318039594669757
 
0.1%

Length

2021-10-28T19:18:00.403281image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-28T19:18:00.575143image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.027534525848877759233
99.9%
36.318039594669757
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

20
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size72.3 KiB
0.0
9240 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.09240
100.0%

Length

2021-10-28T19:18:00.797194image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-28T19:18:00.969079image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.09240
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

21
Real number (ℝ)

HIGH CORRELATION

Distinct26
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.814926276 × 10-17
Minimum-2.358161956
Maximum1.519952093
Zeros0
Zeros (%)0.0%
Negative3359
Negative (%)36.4%
Memory size72.3 KiB
2021-10-28T19:18:01.140923image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2.358161956
5-th percentile-2.358161956
Q1-0.1864180883
median0.5892047214
Q30.5892047214
95-th percentile0.5892047214
Maximum1.519952093
Range3.878114049
Interquartile range (IQR)0.7756228097

Descriptive statistics

Standard deviation1.000054117
Coefficient of variation (CV)5.510163858 × 1016
Kurtosis0.2687364966
Mean1.814926276 × 10-17
Median Absolute Deviation (MAD)0
Skewness-1.227247592
Sum-4.547473509 × 10-13
Variance1.000108237
MonotonicityNot monotonic
2021-10-28T19:18:01.406533image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.58920472145425
58.7%
-0.18641808831203
 
13.0%
-1.11716546513
 
5.6%
-2.358161956465
 
5.0%
-2.047912832358
 
3.9%
1.364827531240
 
2.6%
-2.203037394186
 
2.0%
-0.8069163361175
 
1.9%
-0.4966672122145
 
1.6%
-1.427414584117
 
1.3%
Other values (16)413
 
4.5%
ValueCountFrequency (%)
-2.358161956465
5.0%
-2.203037394186
 
2.0%
-2.047912832358
3.9%
-1.8927882763
 
0.7%
-1.737663708111
 
1.2%
-1.5825391465
 
0.1%
-1.427414584117
 
1.3%
-1.2722900225
 
0.1%
-1.11716546513
5.6%
-0.9620408983
 
< 0.1%
ValueCountFrequency (%)
1.51995209347
 
0.5%
1.364827531240
 
2.6%
1.20970296933
 
0.4%
1.05457840727
 
0.3%
0.899453845383
 
0.9%
0.744329283412
 
0.1%
0.58920472145425
58.7%
0.43408015956
 
0.1%
0.27895559752
 
< 0.1%
0.12383103566
 
0.1%

22
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size72.3 KiB
0.0
9240 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.09240
100.0%

Length

2021-10-28T19:18:01.812751image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-28T19:18:02.047106image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.09240
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

23
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size72.3 KiB
0.0
9240 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.09240
100.0%

Length

2021-10-28T19:18:02.234592image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-28T19:18:02.406459image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.09240
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

24
Real number (ℝ)

HIGH CORRELATION

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.92214545 × 10-16
Minimum-5.923867102
Maximum2.017710871
Zeros0
Zeros (%)0.0%
Negative2144
Negative (%)23.2%
Memory size72.3 KiB
2021-10-28T19:18:02.531450image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-5.923867102
5-th percentile-2.747235913
Q10.4293952765
median0.4293952765
Q30.4293952765
95-th percentile0.4293952765
Maximum2.017710871
Range7.941577973
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.000054117
Coefficient of variation (CV)3.422328334 × 1015
Kurtosis4.360873729
Mean2.92214545 × 10-16
Median Absolute Deviation (MAD)0
Skewness-1.75346558
Sum2.728484105 × 10-12
Variance1.000108237
MonotonicityNot monotonic
2021-10-28T19:18:02.750182image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0.42939527656855
74.2%
-1.1589203181613
 
17.5%
-2.747235913487
 
5.3%
2.017710871241
 
2.6%
-4.33555150724
 
0.3%
-5.92386710220
 
0.2%
ValueCountFrequency (%)
-5.92386710220
 
0.2%
-4.33555150724
 
0.3%
-2.747235913487
 
5.3%
-1.1589203181613
 
17.5%
0.42939527656855
74.2%
2.017710871241
 
2.6%
ValueCountFrequency (%)
2.017710871241
 
2.6%
0.42939527656855
74.2%
-1.1589203181613
 
17.5%
-2.747235913487
 
5.3%
-4.33555150724
 
0.3%
-5.92386710220
 
0.2%

25
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum-0.8753934035
Maximum2.169262198
Zeros0
Zeros (%)0.0%
Negative5328
Negative (%)57.7%
Memory size72.3 KiB
2021-10-28T19:18:03.031413image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-0.8753934035
5-th percentile-0.8753934035
Q1-0.8753934035
median-0.8753934035
Q31.154376998
95-th percentile1.661819598
Maximum2.169262198
Range3.044655602
Interquartile range (IQR)2.029770401

Descriptive statistics

Standard deviation1.000054117
Coefficient of variation (CV)nan
Kurtosis-1.466281114
Mean0
Median Absolute Deviation (MAD)0
Skewness0.5003206112
Sum0
Variance1.000108237
MonotonicityNot monotonic
2021-10-28T19:18:03.218901image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
-0.87539340354642
50.2%
1.1543769982249
24.3%
1.661819598752
 
8.1%
-0.3679508032686
 
7.4%
0.139491797457
 
4.9%
0.6469343973380
 
4.1%
2.16926219874
 
0.8%
ValueCountFrequency (%)
-0.87539340354642
50.2%
-0.3679508032686
 
7.4%
0.139491797457
 
4.9%
0.6469343973380
 
4.1%
1.1543769982249
24.3%
1.661819598752
 
8.1%
2.16926219874
 
0.8%
ValueCountFrequency (%)
2.16926219874
 
0.8%
1.661819598752
 
8.1%
1.1543769982249
24.3%
0.6469343973380
 
4.1%
0.139491797457
 
4.9%
-0.3679508032686
 
7.4%
-0.87539340354642
50.2%

26
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size72.3 KiB
0.0
9240 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.09240
100.0%

Length

2021-10-28T19:18:03.422026image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-28T19:18:03.562644image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.09240
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

27
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size72.3 KiB
-0.6742847689382924
6352 
1.4830529266953025
2888 

Length

Max length19
Median length19
Mean length18.68744589
Min length18

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.6742847689382924
2nd row-0.6742847689382924
3rd row1.4830529266953025
4th row-0.6742847689382924
5th row-0.6742847689382924

Common Values

ValueCountFrequency (%)
-0.67428476893829246352
68.7%
1.48305292669530252888
31.3%

Length

2021-10-28T19:18:03.765756image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-28T19:18:03.968849image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.67428476893829246352
68.7%
1.48305292669530252888
31.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

28
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.151885158 × 10-17
Minimum-2.432191748
Maximum2.310643756
Zeros0
Zeros (%)0.0%
Negative3079
Negative (%)33.3%
Memory size72.3 KiB
2021-10-28T19:18:04.156353image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2.432191748
5-th percentile-1.167435614
Q1-1.167435614
median0.09732052073
Q30.7296985879
95-th percentile1.362076655
Maximum2.310643756
Range4.742835504
Interquartile range (IQR)1.897134202

Descriptive statistics

Standard deviation1.000054117
Coefficient of variation (CV)-1.625605959 × 1016
Kurtosis-1.169494299
Mean-6.151885158 × 10-17
Median Absolute Deviation (MAD)1.264756134
Skewness0.007460396264
Sum-9.094947018 × 10-13
Variance1.000108237
MonotonicityNot monotonic
2021-10-28T19:18:04.375087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0.097320520733407
36.9%
-1.1674356142827
30.6%
1.3620766552172
23.5%
0.7296985879318
 
3.4%
0.4135095543183
 
2.0%
-1.799813681173
 
1.9%
-2.11600271460
 
0.6%
1.99445472247
 
0.5%
1.67826568932
 
0.3%
-0.218868512914
 
0.2%
Other values (6)7
 
0.1%
ValueCountFrequency (%)
-2.4321917481
 
< 0.1%
-2.11600271460
 
0.6%
-1.799813681173
 
1.9%
-1.4836246472
 
< 0.1%
-1.1674356142827
30.6%
-0.851246581
 
< 0.1%
-0.53505754641
 
< 0.1%
-0.218868512914
 
0.2%
0.097320520733407
36.9%
0.4135095543183
 
2.0%
ValueCountFrequency (%)
2.3106437561
 
< 0.1%
1.99445472247
 
0.5%
1.67826568932
 
0.3%
1.3620766552172
23.5%
1.0458876211
 
< 0.1%
0.7296985879318
 
3.4%
0.4135095543183
 
2.0%
0.097320520733407
36.9%
-0.218868512914
 
0.2%
-0.53505754641
 
< 0.1%

Interactions

2021-10-28T19:17:44.234101image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:01.788949image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:05.238226image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:08.672207image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:12.290853image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:16.046671image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:19.829700image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:24.060143image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:28.278516image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:32.554382image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:36.431116image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:40.511191image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:44.539972image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:02.066808image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:05.571622image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:08.954454image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:12.533323image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:16.443463image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:20.206248image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:24.399101image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:28.573882image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:32.856451image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:36.764710image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:40.884496image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:44.798342image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:02.373559image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:05.837854image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:09.212364image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:12.815130image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:16.710374image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:20.525095image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:24.767151image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:28.936288image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:33.155346image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:37.102398image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:41.213990image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:45.105950image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:02.659396image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:06.097456image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:09.479538image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:13.135638image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:17.046851image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:20.930393image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:25.114881image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:29.351197image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:33.490729image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:37.394010image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:41.480760image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:45.447896image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:02.915993image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:06.346202image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:09.752071image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:13.381614image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:17.362615image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:21.208155image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:25.434707image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:29.692883image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:33.817722image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:37.680354image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:41.743061image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:45.787451image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:03.185733image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:06.671762image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:10.146971image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:13.671202image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:17.689854image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:21.569610image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:25.786843image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:30.101023image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:34.100291image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:38.022510image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:42.079837image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:46.118513image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:03.544306image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:06.950365image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:10.518669image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:14.074678image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:17.989334image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:21.914076image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:26.191356image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:30.553221image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:34.470483image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:38.315334image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:42.401381image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:46.517017image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:03.808384image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:07.213446image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:10.865303image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:14.396993image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:18.357051image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:22.184795image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:26.520604image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:30.853303image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:34.795948image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:38.660100image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:42.665461image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:46.796992image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:04.088649image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:07.510681image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:11.139697image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:14.789754image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:18.681076image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:22.489395image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:26.900863image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:31.213306image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:35.089702image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:39.238676image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:42.982838image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:47.093887image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:04.428463image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:07.822454image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:11.421207image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:15.171043image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:18.963388image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:23.089957image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:27.298837image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:31.591757image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:35.438210image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:39.573457image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:43.366114image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:47.400779image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:04.728164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:08.104353image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:11.752752image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:15.466142image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:19.244017image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:23.420248image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:27.583856image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:31.890782image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:35.838674image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:39.920615image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:43.657061image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:47.687448image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:04.998733image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:08.365155image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:12.053297image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:15.797388image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:19.542684image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:23.760620image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:27.913874image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:32.240581image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:36.124511image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:40.251516image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:17:43.938052image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-10-28T19:18:04.671941image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-28T19:18:05.531236image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-28T19:18:06.268971image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-28T19:18:07.034539image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-10-28T19:18:07.612620image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-10-28T19:17:48.291662image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-10-28T19:17:51.161750image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

012345678910111213141516171819202122232425262728
0-0.0721691.731863-1.1087630.585767-0.293755-0.014714-0.7918630.635959-0.1379440.8841890.176454-0.121718-0.016991-0.0389540.0-0.014714-0.010404-0.010404-0.020811-0.0275350.0-1.1171650.00.00.4293951.1543770.0-0.6742850.097321
1-1.1492641.731488-1.1087630.914993-0.293755-0.014714-0.791863-0.732523-0.1379440.8841890.176454-0.121718-0.016991-0.0389540.0-0.014714-0.010404-0.010404-0.020811-0.0275350.0-0.1864180.00.00.4293951.1543770.0-0.674285-1.167436
20.1951371.7311140.469572-1.060366-0.293755-0.0147141.262845-0.732523-0.137944-1.8391130.176454-2.919418-0.016991-0.0389540.0-0.014714-0.010404-0.010404-0.020811-0.0275350.00.5892050.00.0-1.158920-0.8753930.01.483053-1.167436
3-1.5567841.7307390.469572-1.060366-0.293755-0.014714-0.7918631.457048-0.1379440.1061032.354245-0.121718-0.016991-0.0389540.0-0.014714-0.010404-0.010404-0.020811-0.0275350.0-0.1864180.00.00.429395-0.8753930.0-0.6742850.097321
4-1.0292961.7303640.469572-0.401913-0.293755-0.0147141.262845-1.827309-0.1379440.884189-1.275406-0.121718-0.016991-0.0389540.0-0.014714-0.010404-0.010404-0.020811-0.0275350.00.5892050.00.00.429395-0.8753930.0-0.6742850.097321
5-1.2846041.729989-1.1087630.585767-0.293755-0.014714-0.7918630.362263-0.1379440.8841890.176454-0.121718-0.016991-0.0389540.0-0.014714-0.010404-0.010404-0.020811-0.0275350.00.5892050.00.00.429395-0.8753930.0-0.6742850.097321
60.4365741.7296140.469572-0.401913-0.293755-0.0147141.262845-0.732523-0.1379441.273232-2.001336-0.121718-0.016991-0.0389540.0-0.014714-0.010404-0.010404-0.020811-0.0275350.00.5892050.00.0-1.158920-0.8753930.0-0.6742850.097321
7-1.2756071.729239-1.1087630.585767-0.293755-0.014714-0.7918630.362263-0.1379440.8841890.176454-0.121718-0.016991-0.0389540.0-0.014714-0.010404-0.010404-0.020811-0.0275350.00.5892050.00.00.429395-0.8753930.0-0.6742850.097321
81.0769081.7288640.469572-1.060366-0.293755-0.014714-0.791863-0.732523-0.137944-0.4774620.176454-0.121718-0.016991-0.0389540.0-0.014714-0.010404-0.010404-0.020811-0.0275350.00.5892050.00.00.4293951.6618200.01.483053-1.167436
90.6427711.728489-1.108763-0.401913-0.293755-0.014714-0.791863-0.732523-0.137944-1.2555482.354245-0.121718-0.016991-0.0389540.0-0.014714-0.010404-0.010404-0.020811-0.0275350.00.5892050.00.00.429395-0.8753930.0-0.674285-1.167436

Last rows

012345678910111213141516171819202122232425262728
92301.099777-1.7284890.469572-0.401913-0.293755-0.014714-0.791863-0.732523-0.137944-0.671983-2.001336-0.121718-0.016991-0.0389540.0-0.014714-0.010404-0.010404-0.020811-0.0275350.00.5892050.00.0-1.158920-0.8753930.0-0.674285-1.167436
9231-0.689259-1.7288640.469572-0.401913-0.293755-0.0147141.262845-0.732523-0.137944-2.033634-2.001336-0.121718-0.016991-0.0389540.0-0.014714-0.010404-0.010404-0.020811-0.0275350.00.5892050.00.0-1.158920-0.8753930.0-0.674285-1.167436
9232-1.107650-1.7292390.469572-1.060366-0.293755-0.014714-0.7918631.183352-0.137944-0.6719830.176454-0.121718-0.016991-0.0389540.0-0.014714-0.010404-0.010404-0.020811-0.0275350.0-0.1864180.00.0-1.158920-0.8753930.01.4830531.362077
9233-0.855716-1.729614-1.108763-1.060366-0.293755-0.0147141.2628451.183352-0.1379440.8841890.176454-0.121718-0.016991-0.0389540.0-0.014714-0.010404-0.010404-0.020811-0.0275350.00.5892050.00.00.4293951.1543770.0-0.6742851.362077
92340.879709-1.7299890.469572-1.060366-0.293755-0.0147141.2628451.183352-0.137944-1.8391130.176454-0.121718-0.016991-0.0389540.0-0.014714-0.010404-0.010404-0.020811-0.0275350.00.5892050.00.0-1.158920-0.8753930.0-0.6742850.097321
9235-1.375331-1.7303640.469572-1.0603663.404197-0.0147141.262845-1.0062194.104220-0.4774620.176454-0.121718-0.016991-0.0389540.0-0.014714-0.010404-0.010404-0.020811-0.0275350.00.5892050.00.0-1.158920-0.8753930.0-0.674285-1.483625
92360.060922-1.7307390.469572-1.060366-0.293755-0.014714-0.7918631.183352-0.1379440.1061030.176454-0.121718-0.016991-0.0389540.0-0.014714-0.010404-0.010404-0.020811-0.0275350.01.5199520.00.0-1.158920-0.8753930.01.4830531.362077
92370.586535-1.7311140.469572-1.0603663.404197-0.014714-0.7918631.183352-0.137944-1.8391130.176454-0.121718-0.016991-0.0389540.0-0.014714-0.010404-0.010404-0.020811-0.0275350.00.8994540.00.0-1.158920-0.8753930.01.4830531.362077
9238-0.586535-1.7314880.469572-0.401913-0.293755-0.0147141.2628451.183352-0.137944-0.671983-2.001336-0.121718-0.016991-0.0389540.0-0.014714-0.010404-0.010404-0.020811-0.0275350.00.5892050.00.00.4293950.6469340.0-0.6742851.362077
9239-0.529925-1.7318630.469572-1.060366-0.293755-0.0147141.2628451.183352-3.0748271.2732320.176454-0.121718-0.016991-0.0389540.0-0.014714-0.010404-0.010404-0.020811-0.0275350.00.5892050.00.0-1.158920-0.3679510.01.4830530.097321